Position-patch based face hallucination via high-resolution reconstructed-weights representation

Danfeng Wan, Yao Lu*, Javaria Ikram, Jianwu Li

*Corresponding author for this work

Research output: Contribution to journalConference articlepeer-review

Abstract

Position-patch based face hallucination methods aim to reconstruct the high-resolution (HR) patch of each low-resolution (LR) input patch independently by the optimal linear combination of the training patches at the same position. Most of current approaches directly use the reconstruction weights learned from LR training set to generate HR face images, without considering the structure difference between LR and the HR feature space. However, it is reasonable to assume that utilizing HR images for weights learning would benefit the reconstruction process, because HR feature space generally contains much more information. Therefore, in this paper, we propose a novel representation scheme, called High-resolution Reconstructed-weights Representation (HRR), that allows us to improve an intermediate HR image into a more accurate one. Here the HR reconstruction weights can be effectively obtained by solving a least square problem. Our evaluations on publicly available face databases demonstrate favorable performance compared to the previous position-patch based methods.

Original languageEnglish
Pages (from-to)421-430
Number of pages10
JournalLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume9314
DOIs
Publication statusPublished - 2015
Event16th Pacific-Rim Conference on Multimedia, PCM 2015 - Gwangju, Korea, Republic of
Duration: 16 Sept 201518 Sept 2015

Keywords

  • Face hallucination
  • High-resolution reconstruction weights
  • Locality constraints
  • Position patch
  • Super resolution

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